The conventional evaluation of maize seed vigor is a time-consuming and labor-intensive process. By contrast, this study introduces an automated, nondestructive framework for classifying maize seed vigor with different seed DNA repair capabilities using hyperspectral images. The selection of coated maize seeds for our case study also aligned well with practical applications. To ensure the accuracy and reliability of the results, rigorous data preprocessing steps were implemented to extract high-quality information from raw spectral data obtained from the hyperspectral images. In particular, commonly used pretreatment methods were explored. Instead of analyzing all the wavelengths of spectral data, a competitive adaptive reweighted sampling method was used to select more informative wavelengths, optimizing analysis efficiency. Furthermore, this study leveraged machine learning models, enriched through oversampling techniques to address data imbalance at the seed level. The results obtained using a support vector machine with enhanced techniques demonstrated promising results with 100% sensitivity, 96.91% specificity, and a 0.9807 Matthews correlation coefficient (MCC). Thus, this study highlighted the effectiveness of hyperspectral imaging and machine learning in modern seed assessment practices. By introducing a seed vigor classification system that can even accommodate coated seeds, this study offers a potential pathway for empowering seed producers in practical, real-world applications.